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Příklady odhadu stavu a parametrů pro lineární model s rovnoměrně rozloženými inovacemi
Pavelková, Lenka
In this contribution, state-space model with uniformly distributed innovations is introduced and the Bayesian state estimation proposed. The off-line evaluation of the maximum a posteriori probability (MAP) estimate of unknowns in the linear state-space model with uniform innovations reduces to linear programming (LP). The solution provides either estimates of the noise boundary and parameters or of the noise boundary and states. The on-line estimation is obtained by applying LP on the sliding window, i.e., by considering only the fixed amount, say partial, of the newest last data and states items. By swapping between state and parameter estimations, joint parameter and state estimation is obtained. The use of Taylor expansion for approximation of products of unknowns solves also the joint parameter and state estimation. Simulation studies help to get an insight on the potential and restrictions of these heuristic method. This contribution shares the experimentally gained experience with both these solutions of the joint state and parameter estimation.
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Výpočetní aspekty návrhu regulátoru a vyčíslení kvality
Novák, Miroslav
This work contributes to the activity in the Department of Adaptive Systems in Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic to develop a complete design algorithm for advanced controllers such as the LQG one and put them through to real applications. The task of controller tuning is to transform the user specified requirements into the values of the tuning parameters. The system knowledge is incomplete. The Bayesian estimation delivers the parameters not as known numbers but as their probability density function. The important contribution of this work is extending the tuning to the multiple input multiple output (MIMO) controllers, where multiple constraints on particular quantities are considered simultaneously.
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První experimenty s distribuovaným Bayesovským rozhodováním
Šmídl, Václav ; Andrýsek, Josef
Decision-making under uncertainty is a natural part of everyday life of every human being. In societal science, various aspects of decision-making were studied, mostly in the area of psychology. In technical science, the process was formalized using probability theory yielding so called Bayesian theory of decision making. However, one of the key assumptions of this theory is that the decision-maker is the only entity that intentionally influences the system. This assumption is certainly violated in more complicated systems, such as human society or distributed control. Recently, a series of papers attempts to offer an extension of the Bayesian theory for many decision-makers, i.e. decentralized stochastic control. Since there are no proofs of optimality of the proposed Bayesian distributed decision making available in the literature, we study this approach via experimental simulation studies.
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Nastavení kovariancí a stavových parametrů pro Kalmanův filtr
Pecherková, Pavla
The estimation of queue lengths via local filters is depend on initial settings of covariances and state parameters if they are unknown. Using of Subspace methods looks as a good possibilities for initial settings but the traffic problem is so complicated that it is not possible used the Subspace methods generally.
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Faktorizovaný Kalmanův filtr
Suzdaleva, Evgenia
The present work considers the problem of the factorized filtering with Gaussian models and offers the solution, based on applying the L'DL decomposition of the covariance matrix. The results of such filtering is the posterior state estimate with the mean value and the factorized matrix of covariance.
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Prediktivní řízení pro mechatronické laboratorní modely
Belda, Květoslav
The paper deals with the design of discrete adaptive model-based predictive control for simple mechatronic systems. Simple mechatronic systems are considered as Single-Input/Single-Output systems or possibly systems with low number of inputs and outputs. However, the methods of adaptation and model-based control are not generally limited to this condition. In the paper, a combination of on-line identification and generalized predictive control will be introduced. The identification is based on least squares. The predictive control arises from state-space formulation. This idea is applied to ARX models representing Input/Output formulation. The presented algorithms are derived in computationally suitable square-root form and their correctness is documented by tests on laboratory models.
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Bayesovské odhadování délky kolony
Dohnal, Pavel
The paper deals with an application of Bayes for estimation of the queue length in junction arm. In Bayesian view the concept of probability is not interpreted in terms of limits of relative frequencies but more generally as a subjective measure of belief of a rationally and consistently reasoning person which is used to describe quantitatively the uncertain relationship between the statistician and the external world. This model splits controlled networks into microregions. The queue length and the occupancy of each junction approach are the basic state quantities for fully expressed traffic situation at given time instant. The occupancy determines relative time of the detector activation during sample period, i.e. the proportion of time when detector has been occupied and total time of measuring period. The optimization criterion for this attitude is minimization of the queue length. For clearness, the model is derived for simple junction.
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Sborník abstraktů sedmé mezinárodní PhD konference
The scope of the workshop is topically focused on the recent trend in cybernetics, namely, the support for decision making at higher hierarchic levels. This reveals the common feature of seemingly disparate contributions and topics, namely, the complexity.
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